def test_same_result(self, im_shape, input_type):
data = self.get_data(im_shape, input_type)
alpha = [0.5, 0.8]
t = RandGibbsNoised(KEYS, 1.0, alpha)
t.set_random_state(42)
out1 = t(deepcopy(data))
t.set_random_state(42)
out2 = t(deepcopy(data))
for k in KEYS:
torch.testing.assert_allclose(out1[k], out2[k], rtol=1e-7, atol=0)
self.assertIsInstance(out1[k], type(data[k]))
def test_same_result(self, im_shape, as_tensor_output, as_tensor_input):
data = self.get_data(im_shape, as_tensor_input)
alpha = [0.5, 0.8]
t = RandGibbsNoised(KEYS, 1.0, alpha, as_tensor_output)
t.set_random_state(42)
out1 = t(deepcopy(data))
t.set_random_state(42)
out2 = t(deepcopy(data))
for k in KEYS:
np.testing.assert_allclose(out1[k], out2[k])
self.assertIsInstance(
out1[k], torch.Tensor if as_tensor_output else np.ndarray)
def test_same_result(self, im_shape, input_type):
data = self.get_data(im_shape, input_type)
alpha = [0.5, 0.8]
t = RandGibbsNoised(KEYS, 1.0, alpha)
t.set_random_state(42)
out1 = t(deepcopy(data))
t.set_random_state(42)
out2 = t(deepcopy(data))
for k in KEYS:
assert_allclose(out1[k],
out2[k],
rtol=1e-7,
atol=0,
type_test="tensor")